Any references to methods, apparatus or documents of the prior art are not to be taken as constituting any evidence or admission that they formed, or form part of the common general knowledge.
Cough is a defense mechanism of the body to clear the respiratory tract from foreign materials which is inhaled accidentally or produced internally by infections [1]. It is a common symptom in a range of respiratory diseases such as asthma and whooping cough (pertussis) as well as pneumonia, which is the leading cause of death in children under 5 years of age. It has been estimated [2] that pneumonia causes over 1.6 million deaths in this group per year, with more than 97% [3] of cases occurring in the developing world. The world health organization (WHO) also reported that in those countries, pertussis has became one of the major childhood morbidities with an estimated 50 million cases and 300,000 deaths every year [4].
Even though cough is common in respiratory diseases and considered an importance clinical symptom, there is no golden standard to assess it. In a typical consultation session, physicians may listen to several episodes of natural or voluntary coughs, to obtain qualitative information such as the “wetness” of a cough. Such qualitative information is extremely useful in diagnosis as well as the treatment of respiratory diseases. However, manual analysis suffers from operator bias and leads to subjective outcomes.
During the consultation session physicians may also seek quantitative information on coughs, such as the frequency of occurrence of cough events over a given time interval. This information can be used to determine the nature (e.g., acute, chronic) and the severity of coughs as well as to monitor the efficacy of treatment. However, to obtain this information, physicians heavily rely on subjective reports of patients or their carers. There is a great need for an automated device capable of counting the number of coughs, especially in childhood diseases. More importantly, technology capable of automatically extracting cough events from long pediatric recordings is needed in order to facilitate the diagnosis of diseases such as pneumonia, pertussis and asthma.
Several approaches have been taken to develop automated cough counting systems (e.g., Hull Automatic Cough Counter (HACC) [5], Leicester Cough Monitor (LCM) [6], LifeShirt [7], VitaloJAK [8], and PulmoTrack [9]). The performances of these devices are varied. The HACC claimed a sensitivity and specificity of (80%, 96%) [5]. The figures for LifeShirt, Pulmotrack, LCM, and Vitalojak are (78%, 99%), (94%, 96%), (85.7%, 99.9%), and (97.5%, 97.7%) respectively [6, 10-13]. They relied on sound intensity dependent techniques, making them susceptible to variations in recording conditions and the particular instruments used. To the best of the inventors' knowledge, none of these commercial devices have been tested on pediatric populations.
Cough recording on children, especially the younger ones, pose several additional challenges. Younger children are unable to produce voluntary coughs upon request. Any method targeting pediatric populations should be capable of using natural coughs recorded over a period of interest. In pediatric recordings, crying, vocalization, and grunting are found abundantly, intermixed with cough sounds. Consequently, technology developed for adults are unlikely to be optimal for use on children. Another issue in cough recording from children is the cough sound intensity variation. Diseases such as severe pediatric pneumonia can dramatically lower the amplitude of a cough sound. Even in healthy people, cough sounds can have a large dynamic range, covering loud coughs to the barely audible. This condition may make intensity-based techniques unreliable for field use. The performance will also depend on particular sound capturing equipment, calibration status, and measurement protocols used.
Existing commercial cough counting devices such as LifeShirt, Vitalojak, and Pulmotrack employ contact sensors. While the use of contact sensors may have some advantages, they also carry several drawbacks. The intervening musculature severely curtails the bandwidth of a cough recorded using contact sensors; free air systems are immune to this. Contact sensors, compared to non-contact (free-air) microphones are robust against background sound propagated through air. However, they are more vulnerable to sound conducted through tissue and bones; spurious rubbing sounds due to sensor movement can also be an issue. In infectious diseases, elaborate efforts are needed to avoid cross contamination of patients through contact instrumentation. Furthermore, in pediatric subjects, contact sensors can also be difficult to attach because of patient discomfort.
Cough sounds carry critically useful information on the state of the airways. However, the existing devices use method that can detect only the presence of events (“Cough Detection”) but are unable to automatically extract cough events (“Cough Segmentation”) for further analysis. Thus they are limited to the counting of coughs. Cough Segmentation requires, in addition to Cough Detection capabilities, the knowledge on the exact beginning as well as the end of each cough event. It is known that inter-cough gaps, the durations of the coughs, and the amplitude of coughs may carry information related to respiratory diseases [14].
One disease whose symptoms include coughing is pneumonia. Pneumonia is the leading killer of young children around the world. It accounts for more than 19% of under-five child deaths each year. It's a disease of poverty and is strongly related with malnutrition and poor healthcare facilities. As a result childhood pneumonia deaths are critically high in developing countries. Pneumonia is also a problem among the aged people throughout the world.
Pneumonia is defined as an infection in the lungs with accumulation of inflammatory cells and secretions in the alveoli. The common symptoms of the Pneumonia includes, cough, difficulty in breathing, fever, headaches, loss of appetite, runny nose, and wheezing. In severe pneumonia cases young infants struggle for breath and may suffer convulsions, skin pallor, unconsciousness, hypothermia and lethargy.
Pneumonia is a difficult disease to diagnose. Current methods of diagnosis include clinical examination (eg: physical signs, chest auscultation), bio-chemical testing (eg: sputum analysis, oxygen saturation) and medical imaging (eg: chest X-rays and in some cases X-ray CT).
What are the Problems with Current Diagnostic Method?
Chest X-ray (CXR) is considered as a commonly available reference standard for diagnosing pneumonia. However, it is not a golden standard. In early stages of the disease, or when the disease involves a part of the lung not easily seen in CXR, pneumonia can be difficult to diagnose using CXR alone. Moreover, sometimes CXR results can be misleading due to lung scarring or congestive heart failure, which can mimic pneumonia in CXR. Even though X-ray CT may provide better outcomes, they are not widely available even in tertiary care hospitals in developing countries. Sputum tests require laboratory cultures and can take a minimum 2-3 days making them too slow for initial diagnosis. A positive sputum test does not necessarily indicate the presence of pneumonia because many of the pathogens causing pneumonia are naturally present in the throats of healthy people. Therefore, sputum test is mainly done to check the sensitiveness of a particular antibiotic that has already been started on a patient. The clinical examination together with the chest auscultation via stethoscopes is the frontline approach used in the initial diagnosis of pneumonia in a clinical setting; X-ray may be used to confirm a diagnosis when available.
None of the methods described above are available for mass deployment in remote regions of the world where pneumonia is rampant. They are expensive, resource intensive and require trained medical professionals to perform them.
In order overcome this problem, World Health Organization (WHO) has developed a set of highly simplified guidelines [ ref. 3] to diagnose childhood pneumonia in resource poor and remote areas of the world. According to these, a child presenting with difficult breathing or cough is diagnosed with pneumonia if they have tachyponea (fast breathing). Fast breathing is defined as 60 breaths or more in infants less than 2 months, 50 breaths or more per minute for the infants between 2 months to 12 months and 40 breaths or more per minute for children age between 12 months to 5 year old [ref 3, 4]. Chest in-drawing, skin pallor and unconsciousness may indicate severe pneumonia and also belong in WHO Danger Signs. This system is easier to implement in the field and is designed to have a high sensitivity of diagnosis (about 90% patients with the disease are picked up). However, WHO guidelines suffer from poor specificity of diagnosis; a large number of patients without pneumonia are also picked up as having pneumonia. The specificity of WHO algorithm is known to be about 20%.
Though WHO guidelines have helped in reducing the mortality rate down to 1.6 million childhood deaths per annum, several problems remain with the method. Due to its low specificity[ref.6], a large number of non-pneumonic children are receiving antibiotics unnecessarily. This has resulted in treatment failures arising from community antibiotic resistance. In many pneumonia endemic regions diseases such as malaria are also common. Both pneumonia and malaria share symptoms of fever, fast breathing and cough, and WHO algorithm for pneumonia can lead to misdiagnosis and delay in treatment. Several other diseases/conditions (such as COPD, asthma, pulmonary edema, lung cancer etc), which do not require antibiotics, can present with similar clinical features to pneumonia.
To improve the specificity of the WHO criteria, Cardoso et al [ref 6] suggested including presence of fever to diagnose pneumonia. They showed that adding fever improves diagnostic specificity significantly (up to 50%). Several other researchers in the past have assessed the accuracy of the WHO criteria in childhood pneumonia diagnosis. Harari et al [ref 7] studied several variables including tachypnoea to determine which clinical signs best predict radiographic evidence of pneumonia, in 185 children. They reported sensitivity of 73% and 64% specificity in diagnosing pneumonia with only tachypnoea (respiratory rate (RR) 50≥breaths/min for kids<12 months and RR≥40 breaths/min if age 1 year or older) as predictor. When they added chest indrawing to tachypnoea sensitivity improved by 4% at the cost of specificity (dropped by 6%). Similarly with the other clinical symptoms such as nasal flaring, fever, sleeping poorly cough>2 days etc, sensitivity and specificity varied between 20 to 90%[ref 6-10]. High sensitivity was achieved at the cost of specificity and vice-versa.
It is an object of a first aspect of the present invention to provide an improved method for identifying cough sounds.
Furthermore, it is an object of a further aspect of the present invention to provide a method for diagnosis of particular disease states, for example pneumonia, asthma and rhinopharyngitis from cough sounds.